Triple
T28081956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Workforce1 Career Centers network |
E709703
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
Workforce1 Career Center in each New York City borough
Workforce1 Career Center in each New York City borough is a local job assistance hub that provides residents with employment services such as job matching, career counseling, and skills training.
|
E1802651
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Workforce1 Career Center in each New York City borough | Statement: [Workforce1 Career Centers network, hasComponent, Workforce1 Career Center in each New York City borough]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Workforce1 Career Center in each New York City borough Triple: [Workforce1 Career Centers network, hasComponent, Workforce1 Career Center in each New York City borough]
Generated description
Workforce1 Career Center in each New York City borough is a local job assistance hub that provides residents with employment services such as job matching, career counseling, and skills training.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ef9b7037f0819095bb90eaccbcaf32 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f64045bc2c8190b847de0b6c11a6e3 |
completed | May 2, 2026, 6:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a15c9166b2481909791a9198eb90178 |
completed | May 26, 2026, 4:23 p.m. |
| NEDg | Description generation | batch_6a15caed4b148190b42af70ea0f6d872 |
completed | May 26, 2026, 4:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a15cbdf46208190916381816f411f87 |
completed | May 26, 2026, 4:35 p.m. |
Created at: April 27, 2026, 8:52 p.m.